Consistent estimators for the covariance operator are crucial for FPCA.
The consistency and convergence rates of estimators for covariance operators
were thoroughly studied for differently dense data settings [1]}, [2]},
their properties in the presence of stronger, potentially non-Gaussian noise, however,
remain an area of active research.
Standard techniques for covariance estimation quickly become computationally infeasible in high-dimensional [3]} or irregular [4]} data settings and algorithmic innovations are required.
Recently, [5]} introduced promising neural network architectures for the efficient, nonparametric approximation
of (multidimensional) covariance operators and their eigen-decomposition.
